def demo():
A = np.random.randn(100, 200)
X = np.random.randn(100, 100)
lamb = 0.01
print("solve min |Z|_* + lambda |E|_21 s.t. X = AZ + E by exact ALM ...")
tic = time.time()
Z1, E1 = solve_lrr(X, A, lamb, reg=0, alm_type=0)
obj1 = np.sum(np.linalg.svd(Z1)[1]) + lamb * np.sum(np.sqrt(np.sum(E1 ** 2,
1)))
print("Elapsed time:", time.time() - tic)
print("objective value=", obj1)
print("solve min |Z|_* + lambda |E|_21 s.t. X = AZ + E by inexact ALM ...")
tic = time.time()
Z2, E2 = solve_lrr(X, A, lamb, reg=0, alm_type=1)
obj2 = np.sum(np.linalg.svd(Z2)[1]) + lamb * np.sum(np.sqrt(np.sum(E2 ** 2,
1)))
print("Elapsed time:", time.time() - tic)
print("objective value=", obj2)
diff = np.max(np.abs(Z1 - Z2))
print("### Warning: difference of the solution found by those two \
approaches: |Z1 - Z2|_inf=%f" % diff)
print("solve min |Z|_* + lambda |E|_1 s.t. X = AZ + E by exact ALM ...")
tic = time.time()
Z1, E1 = solve_lrr(X, A, lamb, reg=1, alm_type=0)
obj1 = np.sum(np.linalg.svd(Z1)[1]) + lamb * np.sum(np.sqrt(np.sum(E1 ** 2,
1)))
print("Elapsed time:", time.time() - tic)
print("objective value=", obj1)
print("solve min |Z|_* + lambda |E|_1 s.t. X = AZ + E by inexact ALM ...")
tic = time.time()
Z2, E2 = solve_lrr(X, A, lamb, reg=1, alm_type=1)
obj2 = np.sum(np.linalg.svd(Z2)[1]) + lamb * np.sum(np.sqrt(np.sum(E2 ** 2,
1)))
print("Elapsed time:", time.time() - tic)
print("objective value=", obj2)
diff = np.max(np.abs(Z1 - Z2))
print("### Warning: difference of the solution found by those two\
approaches: |Z1 - Z2|_inf=", diff)
def demo():
A = np.random.randn(100, 200)
X = np.random.randn(100, 100)
lamb = 0.01
print "solve min |Z|_* + lambda |E|_21 s.t. X = AZ + E by exact ALM ..."
tic = time.time()
Z1, E1 = solve_lrr(X, A, lamb, reg=0, alm_type=0)
obj1 = np.sum(np.linalg.svd(Z1)[1]) + lamb * np.sum(np.sqrt(np.sum(E1 ** 2,
1)))
print "Elapsed time:", time.time() - tic
print "objective value=", obj1
print "solve min |Z|_* + lambda |E|_21 s.t. X = AZ + E by inexact ALM ..."
tic = time.time()
Z2, E2 = solve_lrr(X, A, lamb, reg=0, alm_type=1)
obj2 = np.sum(np.linalg.svd(Z2)[1]) + lamb * np.sum(np.sqrt(np.sum(E2 ** 2,
1)))
print "Elapsed time:", time.time() - tic
print "objective value=", obj2
diff = np.max(np.abs(Z1 - Z2))
print "### Warning: difference of the solution found by those two \
approaches: |Z1 - Z2|_inf=%f" % diff
print "solve min |Z|_* + lambda |E|_1 s.t. X = AZ + E by exact ALM ..."
tic = time.time()
Z1, E1 = solve_lrr(X, A, lamb, reg=1, alm_type=0)
obj1 = np.sum(np.linalg.svd(Z1)[1]) + lamb * np.sum(np.sqrt(np.sum(E1 ** 2,
1)))
print "Elapsed time:", time.time() - tic
print "objective value=", obj1
print "solve min |Z|_* + lambda |E|_1 s.t. X = AZ + E by inexact ALM ..."
tic = time.time()
Z2, E2 = solve_lrr(X, A, lamb, reg=1, alm_type=1)
obj2 = np.sum(np.linalg.svd(Z2)[1]) + lamb * np.sum(np.sqrt(np.sum(E2 ** 2,
1)))
print "Elapsed time:", time.time() - tic
print "objective value=", obj2
diff = np.max(np.abs(Z1 - Z2))
print "### Warning: difference of the solution found by those two\
approaches: |Z1 - Z2|_inf=", diff
def LowRank(source, target):
A = source.numpy()
X = target.numpy()
lamb = 0.01
loss = 0
#print("solve min |Z|_* + lambda |E|_1 s.t. X = AZ + E by exact ALM ...")
#tic = time.time()
#Z1, E1 = solve_lrr(X, A, lamb, alm_type=0)
#obj1 = np.sum(np.linalg.svd(Z1)[1]) + lamb * np.sum(np.sqrt(np.sum(E1 ** 2, 1)))
#print("Elapsed time:", time.time() - tic)
#print("objective value=", obj1)
#print("solve min |Z|_* + lambda |E|_1 s.t. X = AZ + E by inexact ALM ...")
tic = time.time()
Z2, E2 = solve_lrr(X, A, lamb, alm_type=1)
obj2 = np.sum(
np.linalg.svd(Z2)[1]) + lamb * np.sum(np.sqrt(np.sum(E2**2, 1)))
#print("Elapsed time:", time.time() - tic)
#print("objective value=", obj2)
#diff = np.max(np.abs(Z1 - Z2))
loss = np.max(np.abs(Z2))
print("low-rank loss", loss)
return loss
import numpy as np
import sys
sys.path.insert(0, "/home/adriano/workspace/lrr")
from solve_lrr import solve_lrr
data = np.loadtxt("/tmp/data")
X = data.T
A = X
l = 0.1
Z, E = solve_lrr(X, A, l, reg=1, alm_type=1, display=True)
np.savetxt("/tmp/Z", Z)
np.savetxt("/tmp/E", E)
import numpy as np
import sys
sys.path.insert(0, '/home/adriano/workspace/lrr')
from solve_lrr import solve_lrr
data = np.loadtxt('/tmp/data')
X = data.T
A = X
l = 0.1
Z, E = solve_lrr(X, A, l, reg=1, alm_type=1, display=True)
np.savetxt('/tmp/Z', Z)
np.savetxt('/tmp/E', E)